1. Field of the Invention
Embodiments of the invention are directed toward an apparatus and methods for an effective blind, passive, splicing/tampering detection. In particular, various embodiments of the invention relate to apparatus and methods for the use of a natural image model to detect image splicing/tampering where the model is based on statistical features extracted from a given test image and multiple 2-D arrays generated by applying the block discrete cosine transform (DCT) with several different block-sizes to the test images.
2. Description of Background Art
Replacing one or more parts of a host picture with fragment(s) from the same host picture or other pictures is called a photomontage or image tampering. Image tampering may be defined as a malicious manipulation of an image to forge a scene that actually never happened in order to purposely mislead observers of the image.
Image splicing is a simple and commonly used image tampering scheme for the malicious manipulation of images to forge a scene that actually never exists in order to mislead an observer. Image splicing is often a necessary step in image tampering. In image splicing, a new image is formed by cropping and pasting regions from the same or different image sources. Modern digital imaging techniques have made image splicing easier than ever before. Even without post-processing of the image, image splicing detection can hardly be caught by human visual systems. Hence, high probability of detection of image splicing detection is urgently needed to tell if a given image is spliced without any a priori knowledge. That is, image splicing/tampering detection should be blind in nature.
Researchers recently have made efforts on image splicing detection due to the increasing needs of legal forensics. For example, a method on blind splicing detection has been reported in T.-T. Ng, S.-F. Chang, and Q. Sun, “Blind detection of photomontage using higher order statistics,” IEEE International Symposium on Circuits and Systems 2004, Vancouver, BC, Canada, May, 2004. However, the reported results of a 72% success rate for tampering detection over the Columbia Image Splicing Detection Evaluation Dataset are not satisfactory for image splicing/tampering detection.
Another background art approach, utilizing statistical moments of characteristic functions has been reported in Y. Q. Shi, G. Xuan, D. Zou, J. Gao, C. Yang, Z. Zhang, P. Chai, W. Chen, and C. Chen, “Steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network”, IEEE International Conference on Multimedia & Expo 2005, Amsterdam, Netherlands, July, 2005. In this approach, 78-dimensional (78-D) feature vectors are used for universal steganalysis. The first half of features are generated from the given test image and its 3-level Haar wavelet decomposition. The second half of features are derived from the prediction-error image and its 3-level Haar wavelet decomposition. Considering the image and its prediction-error image as the LL0 (Low-Low 0) subbands, there are 26 subbands totally. The characteristic function (CF) (i.e., the discrete Fourier transform (DFT) of the histogram) of each of these subbands is calculated. The first three moments of these CFs are used to form the 78-D feature vectors. The above-mentioned steganalysis scheme provides good results when attacking data hiding algorithms operating in the spatial domain but further improvement in detection probability performance is desirable.
Based on the above discussed features, a more advanced technology has been developed in C. Chen, Y. Q. Shi, and W. Chen, “Statistical moments based universal steganalysis using JPEG 2-D array and 2-D characteristic function,” IEEE International Conference on Image Processing 2006, Atlanta, Ga., USA, Oct. 8-11, 2006. In this background art method, 390-dimensional (390-D) feature vectors are developed for universal steganalysis. These 390-D features consist of statistical moments derived from both image spatial 2-D array and JPEG 2-D array, formed from the magnitudes of JPEG quantized block discrete cosine transform (DCT) coefficients. In addition to the first order histogram, the second order histogram is also considered and utilized. Consequently, the moments of 2-D CF's are included for steganalysis. Extensive experimental results have shown that this steganalysis method outperforms in general the background art in attacking modern JPEG steganography including OutGuess, F5, and MB1. However, as with the above, further improvements in detection performance is desirable.
In our own background art developments, we have developed a powerful steganalyzer to effectively detect the advanced JPEG steganography in Y. Q. Shi, C. Chen, and W. Chen, “A Markov process based approach to effective attacking JPEG steganography”, Information Hiding Workshop 2006, Old Town Alexandria, Va., USA, Jul. 10-12, 2006. In this work, we first choose to work on the image JPEG 2-D array. Difference JPEG 2-D arrays along horizontal, vertical, and diagonal directions are then used to enhance changes caused by JPEG steganography. Markov processes are then applied to modeling these difference JPEG 2-D arrays so as to utilize the second order statistics for steganalysis. In addition to the utilization of difference JPEG 2-D arrays, a thresholding technique is developed to greatly reduce the dimensionality of transition probability matrices, i.e., the dimensionality of feature vectors, thus making the computational complexity of the proposed scheme manageable. Experimental results have demonstrated that this scheme has outperformed the existing steganalyzers in attacking OutGuess, F5, and MB1 by a significant margin. However, as with the above, further improvement in detection performance is desirable.
From the discussion above, it is clear that image splicing detection is of fundamental importance in the art of image splicing/tampering detection. The blind splicing detection methods of the background art have typically achieved a probability of successful detection rate of 72%-82% against the Columbia Image Splicing Detection Evaluation Dataset. Thus, there is a need in the art for further improvement in image splicing/tampering detection performance with blind methods for authenticating images.